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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 469 records · Page 26

All sources NASA NTRS DOE OSTI

DOE OSTI2023

ESnet/JLab FPGA Accelerated Transport

To increase the science rate for high data rates/volumes, Thomas Jefferson National Accelerator Facility (JLab) has partnered with Energy Sciences Network (ESnet) to define an edge to data center traffic shaping / steering transport capability featuring data event aware network shaping and forwarding. The keystone of this ESnet+JLab FPGA Accelerated Transport (EJFAT) is the joint development of an AI/ML directed dynamic compute work Load Balancer (LB) of UDP streamed data. The LB is a suite consisting of a Field Programmable Gate Array (FPGA) executing the dynamically configurable, low fixed latency LB data plane featuring real-time packet redirection and high throughput, and a control plane running on the FPGA host computer that monitors network and compute farm telemetry in order to make dynamic AI/ML guided decisions for destination compute host redirection/load balancing and destination resource provisioning. The LB provides for three-tier horizontal scaling across LB suites, core compute hosts, and CPUs within a host. The LB effectively provides seamless integration of edge/core computing to support direct experimental data processing for immediate use by JLab science programs and others such as the EIC as well as data centers of the future requiring high throughput and low latency for both hot and cooled data for both running experiment data acquisition systems and data center use cases.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

A Data Science and Machine Learning Platform Supporting Large Particle Accelerator Control and Diagnostics Applications Final Report: SBIR Initial Phase II DE-SC0022583

The Machine Learning Data Platform (MLDP) is a product providing full-stack support for data science, Machine Learning, and Artificial Intelligence (ML/AI) applications at particle accelerator and large experimental physics facilities. It supports ML/AI applications from front-end, high-speed acquisition of heterogeneous, time-series data, through data archiving and management, to back-end analysis. The MLDP embodies a “data-science ready” platform for data analysis and ML/AI applications in diagnosis, modelling, control, and optimization of these facilities. It provides data scientists and applications a consistent, datacentric interface to archive data standardizing implementation and deployment of ML/AI algorithms to different operations configurations within the same facility, or between facilities. Being an open-source, public-domain project, the MLDP is intended for broadest possible impact by increasing accessibility and minimizing the required expertise for installation and operation. The MLDP can also be deployed at user facilities for experimental data collection, archiving, and analysis. It is capable of acquisition and archiving of heterogeneous data from experimental equipment (e.g., images, arrays, structures, etc.) along with system hardware configurations (e.g., scalars, tables), control system process variables, and any metadata required for provenance. Thus, the MLDP can manage experimental data through its entire lifecycle, from acquisition and archiving, through analysis and investigation, to release and final publication.

43 PARTICLE ACCELERATORS↗

DOE OSTI2021

Data from : "Warming promotes loss of subsoil carbon through accelerated degradation of plant-derived organic matter". Blodgett warming experiment

This dataset contains data used for the paper: Warming promotes loss of subsoil carbon through accelerated degradation of plant-derived organic matter. Soil Biology and Biochemistry 156, 108185. doi:10.1016/j.soilbio.2021.108185On April 2018 (after 4.5 years of warming), we collected O-horizon (organic horizon) material and soil core down to 90 cm depth from a whole-soil warming experiment in a mixed-coniferous temperate forest, located at the University of California Blodgett experimental forest in the foothills of the Sierra Nevada, CA USA. The goal was to assess how 4.5 years of +4 °C whole-soil warming affected the quantity and quality of soil organic matter. Cores were collected from 6 experimental plots (three replicated blocks); samples were collected in 10 cm increments from 0 to 90 cm depth.This dataset contains a compressed (.zip) archive of the data used for this manuscript. The dataset includes files in .xlsx format, which can be accessed and processed using MS Excel or R. Carbon and nitrogen concentrations, as well as stable carbon isotope composition ((δ13C) and solvent extractable lipid biomarker (alkanoic acids and alkanes) data are provided as processed data files. Diffuse reflectance infrared Fourier transform (DRIFT) spectroscopy data are provided as raw output and processed data files.The dataset files "Blodgett_warming_Data" and "Blodgett_warming_raw_data.xlsx" were updated on July 7, 2021. The following updates were made: (1) specification of the units of measurements in the dataset (the unit of measurement was missing in some parameters) and (2) the column name "pulled depth" was changed to "pooled depth" in the updated data files.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

COVID-19, An Exercise in Data Governance at Sandia National Laboratories

In April of 2020, Sandia National Laboratories had an urgent need to identify and manage the data that could be used to create mobile applications, models, reports, and visualizations to assist management in safely bringing the workforce onsite during the COVID-19 pandemic. Multiple divisions volunteered to design and build software solutions; meanwhile, requests for new data sources, including duplicate requests, were inundating Information Technology (IT) and data owners. The Enterprise Data Governance Team was assigned to resolve obtaining and accessing new sources of data in an accelerated timeframe. Through successful collaboration with multiple stakeholders and domain owners across Sandia, the Enterprise Data Governance Team rapidly developed a centralized data strategy and solution for use in safeguarding the Sandia workforce during the COVID-19 pandemic. This foundation enabled teams to successfully develop solutions, including reports for executives and management as well as the data for modeling and scientific analysis.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2022

Correcting Magnetic-Field Diffusion Effects in Beam Position Monitors

Beam position monitors (BPMs) provide timeresolved measurements of the current and centroid position of high-current electron beams in linear induction accelerators (LIAs). The data from some types of BPMs can be influenced by magnetic field diffusion into the surrounding metal. We derive an estimate of the correction factor from first principles, and show how it is applied in practice to nearly eliminate the effect from the data.

43 PARTICLE ACCELERATORS↗

NASA NTRS2011

Assessment of an Automated Touchdown Detection Algorithm for the Orion Crew Module

Orion Crew Module (CM) touchdown detection is critical to activating the post-landing sequence that safe?s the Reaction Control Jets (RCS), ensures that the vehicle remains upright, and establishes communication with recovery forces. In order to accommodate safe landing of an unmanned vehicle or incapacitated crew, an onboard automated detection system is required. An Orion-specific touchdown detection algorithm was developed and evaluated to differentiate landing events from in-flight events. The proposed method will be used to initiate post-landing cutting of the parachute riser lines, to prevent CM rollover, and to terminate RCS jet firing prior to submersion. The RCS jets continue to fire until touchdown to maintain proper CM orientation with respect to the flight path and to limit impact loads, but have potentially hazardous consequences if submerged while firing. The time available after impact to cut risers and initiate the CM Up-righting System (CMUS) is measured in minutes, whereas the time from touchdown to RCS jet submersion is a function of descent velocity, sea state conditions, and is often less than one second. Evaluation of the detection algorithms was performed for in-flight events (e.g. descent under chutes) using hi-fidelity rigid body analyses in the Decelerator Systems Simulation (DSS), whereas water impacts were simulated using a rigid finite element model of the Orion CM in LS-DYNA. Two touchdown detection algorithms were evaluated with various thresholds: Acceleration magnitude spike detection, and Accumulated velocity changed (over a given time window) spike detection. Data for both detection methods is acquired from an onboard Inertial Measurement Unit (IMU) sensor. The detection algorithms were tested with analytically generated in-flight and landing IMU data simulations. The acceleration spike detection proved to be faster while maintaining desired safety margin. Time to RCS jet submersion was predicted analytically across a series of simulated Orion landing conditions. This paper details the touchdown detection method chosen and the analysis used to support the decision.

Gay, Robert S.↗

NASA NTRS1986

Digital filtering and acceleration pulse interpretation

It was concluded that the post processing digital filter is quite effective for removing unwanted high frequency and noise without distorting the signal. In addition, digital filtering is effective for smoothing low-level low resolution digital data where the digital stairstep phenomena are pronounced. Integrating the acceleration data to obtain velocity traces is quite useful. The velocity change provides a check on the validity of the acceleration trace. (Zero offsets in acceleration must be removed before integrating.) Average accelerations can be obtained from the velocity trace by dividing the change in velocity for an acceleration pulse by the pulse duration. One can obtain the peak of an equivalent triangular pulse by doubling the average acceleration.

Pasanella, E. L.↗

DOE OSTI2022

Reliability Evaluation of Bifacial and Monofacial Glass/Glass Modules with EVA and non-EVA Encapsulants

The market share for bifacial modules is projected to be doubled from 30% to 60% in the next ten years. For the monofacial crystalline silicon glass/backsheet (G/B) modules, extensive reliability data has been available for over 40 years. However, practically little/no long-term field or accelerated reliability test data is available for the new generation glass/glass (G/G) bifacial modules and glass/transparent (G/T) bifacial modules. Therefore, the primary motivation of this project was to identify the reliability strengths and weaknesses of new generation G/G modules compared to G/B modules. In this 3-year project, the goal was to objectively recommend the best construction materials for the new generation G/G (bifacial) modules through a systematic experimental approach with appropriate tasks including: Evaluation of field retrieved old-generation G/G modules; Inspection of new-generation G/G modules installed in the plants; Evaluation of new-generation G/G, G/B and G/T modules using EAST (Extended Accelerated Stress Testing), CAST (Combined Accelerated Stress Testing) and FAST (Field Accelerated Stress Testing); and, dynamic literature search and review. Major accomplishments and outcomes of this project are: Accomplishments: Evaluated more than 60 field retrieved modules and constructed and characterized more than 135 mini-modules with three substrate types (G, B, and T), two encapsulant types (EVA and POE), and two cell types (monofacial and bifacial) as well as evaluated more than 30 commercial G/G and G/B modules; Subjected all the modules to multitude characterization tests and various indoor and outdoor accelerated stress tests including FAST (Field Accelerated Stress Testing), EAST (Extended Accelerated Stress Testing), and CAST (Combined Accelerated Stress Testing) to identify and correlate the failure modes in both field and lab tests. Outcomes: Based on the accelerated test results and handling/mounting experience obtained in this project, the recommended best construction for the glass/glass modules is: “Framed GG modules with cut-cells and POE encapsulant (UVpass front; UVpass back).” However, from a statistical and manufacturing point of view (along with stakeholders surveys), the following cautionary notes are added to the above-mentioned recommendation: (i) Cut cells could introduce a higher level of manufacturing issues, including a higher number of cell interconnects; (ii) POE encapsulant is more expensive than EVA and could present a delamination risk and lower throughput during manufacturing due to lower adhesion strength. Other encapsulants, such as coextruded EPE (EVA/POE/EVA), are also recommended to be investigated. The potential public benefit of the proposed project is to present the strengths and weaknesses of glass/glass modules, so an informed procurement decision can be made.

14 SOLAR ENERGY↗

DOE OSTI2023

Accelerated Materials Design for Molten Salt Technologies Using Innovative High-Throughput Methods

The focus of the project is on building an innovative accelerated materials design platform for molten salt technologies using novel high-throughput methods coupled to data analytics. The main objectives is to predict a FeCrMnNi alloy compositional space with better corrosion resistance than stainless steel 316 and identify new molten salt corrosion mechanisms. The project demonstrates the feasibility to use high-throughput methods coupled to data analytics to accelerate alloy design for extreme environments applications. Using a trained and tested machine learning (ML) model, 2000 FeCrMnNi alloy corrosion rate in molten chloride salts were predicted and a compositional field with corrosion rate lower than 316 stainless steel was identified. The ML model interpretability unveiled multiple features of importance in the model prediction. Some features were expected to be of relative significance, such as work function, surface energy and alloy electronegativity, and the ML model interpretability analysis confirmed those. On the other hand, the most important feature is the diffusion coefficient of Ni in the bulk alloy which indicates that a surface diffusion mechanism plays an important role in the overall corrosion mechanism in molten salts.

36 MATERIALS SCIENCE↗

NASA NTRS2020

On Development of Three-Dimensional Visualization Capabilities in Glenn Research Center Communication Analysis Suite

With NASA’s upcoming mission to return to the Moon sustainably by 2024 and using that success as a means to step onto the barren world of Mars, it remains more important than ever to conduct research and planning as thoroughly and efficiently as possible. In a mission as complex as landing humans onto another celestial body, a network of orbiting satellites and ground stations must accurately and reliably communicate with each other, enabling crucial data communications throughout the mission. Visualizing this important data communication increases the understanding of the data and can accelerate analyses efforts. The purpose of this software development is to create an interactive visualization with data taken MATLAB® scripts in the GRC Communication Analysis Suite that is easy to understand, can show all necessary data, and display the data accurately. The main types of data to visualize are from the State Propagation, Line of Sight and Dynamic Link Margin scripts. These all show positions and orbits of satellites and ground stations, while the Line of Sight data shows when they have the ability to communicate with each other based on their respective antenna positions and fields of view. Additionally, the Dynamic Link Margin mode color-codes the communication link performance onto the Line of Sight access lines. Visualization requires a graphics language that is easily accessible, has the needed features, and able to easily read data produced by the GRC Communication Analysis Suite MATLAB® scripts. ThreeJS, a graphics library for Web Graphics Library, coded in JavaScript was selected for the visualization. The next part of the software development was to move the data from MATLAB® to the JavaScript. The best way to accomplish this was to implement a MATLAB® function converting the output data of the scripts to a JavaScript Object Notation file. A key part of the development was creating the visualization within JavaScript and ThreeJS to visualize any combination of planets, moons, orbits, satellites, ground stations, line of sight links, and handle future features without changing major parts of the code. The current visualization capability runs directly from MATLAB®, and can dynamically create any scene. This software development currently supports the lunar communications analysis underway by NASA, and can be easily expanded upon in the future to aid any analysis requirements to help plan current and future space missions.

Visualization↗

NASA NTRS1989

Seed population for about 1 MeV per nucleon heavy ions accelerated by interplanetary shocks

Data obtained between 1977 and 1982 by the ISEE 1 and ISEE 3 satellites on the composition of heavy ions of about 1 MeV per nucleon, accelerated in interplanetary shock events which followed solar flare events, are examined. It was found that the average relative abundances for C, O, and Fe in the shock events were very close to those found for energetic ions in the solar flares, suggesting that, at these energies, the shock accelerated particles have the solar energetic particles as their seed population. This hypothesis is supported by the fact that the Fe/O ratio in the solar particle events is very strongly correlated with the Fe/O ratio in associated diffusive shock events.

Tan, L. C.↗

NASA NTRS1997

An Acceleration Measurement Capability on International Space Station Supporting Microgravity Science Payloads

The conditions present in low-earth-orbit have enabled various scientific investigations to be conducted. A fundamental reason that space is a useful environment for science is the presence of a decreased effect of gravity while in orbital free-fall. Science investigations are conducted to study phenomena occurring as a result of reductions in buoyancy forces and phenomena enabled as a result of the apparent lack of gravity. The science community requires knowledge of the environment in which their experiments are being conducted. A general purpose measurement system was developed in the 1980's to supply scientific investigators with data of the residual acceleration environment. The Space Acceleration Measurement System, or SAMS, has flown aboard the NASA shuttle fleet over fourteen times, successfully acquiring acceleration measurement data in support of microgravity science experiments. As NASA continues its development of the International Space Station and its associated research payloads, it once again becomes necessary to address the requirement to measure the residual acceleration in support of the planned scientific investigations. As a follow-on to the SAMS project, SAMS-2 was initiated to investigate the requirements and develop the measurement system to support the microgravity science payloads aboard the International Space Station. The characteristics of the payload complement to be supported by SAMS-2 and the different capabilities provided by the station requires a fresh look at the design implementation of a general purpose measurement system. Not only is there a requirement for measurements in support of numerous, potentially simultaneous experiments, but this support is required to be provided for at least ten years of on-orbit operation as compared to a ten to fourteen day mission on shuttle. These differences in fundamental requirements result in the design configuration of the space station-based SAMS-2 being significantly different than that of the shuttle-based SAMS. This paper describes these differences in requirements and the planned features to be provided to the science user community. Some of the unique challenges faced in developing a general purpose measurement system are also discussed.

Sutliff, Thomas J.↗

DOE OSTI2026

Continual Learning for Production-Level Machine Learning in Particle Accelerators

Particle accelerators operate in complex environments where data distribution can change dynamically, leading to data drifts that significantly challenge Machine Learning (ML) models. These non-stationary conditions often cause ML models to deteriorate in performance, making it difficult to maintain reliable predictions in operation. The primary sources of data drifts are changes in accelerator settings and changes in equipment performance which cannot be measured directly. To bridge this gap between ML development and long-term deployment in operational settings, we identify key areas within particle accelerators where continual learning can help mitigate drift-induced performance degradation. We will provide a practical guide on selecting the appropriate method given resource constraints and desired stability plasticity trade offs. As a concrete example, we will present a real-world use case for anomaly detection to predict errant beams at the Spallation Neutron Source accelerator, where continual learning has been employed to demonstrate stable performance on drifting data streams. We will present practical challenges, lessons learned, and the results from the deployed ML model.

Rajput, Kishansingh [Thomas Jefferson National Acc↗

DOE OSTI2020

Rapid Identification of X-ray Diffraction Patterns Based on Very Limited Data by Interpretable Convolutional Neural Networks

Large volumes of data from material characterizations call for rapid and automatic data analysis to accelerate materials discovery. Herein, we report a convolutional neural network (CNN) that was trained based on theoretical data and very limited experimental data for fast identification of experimental X-ray diffraction (XRD) patterns of metal–organic frameworks (MOFs). To augment the data for training the model, noise was extracted from experimental data and shuffled; then it was merged with the main peaks that were extracted from theoretical spectra to synthesize new spectra. For the first time, one-to-one material identification was achieved. Theoretical MOFs patterns (1012) were augmented to a whole data set of 72 864 samples. It was then randomly shuffled and split into training (58 292 samples) and validation (14 572 samples) data sets at a ratio of 4:1. For the task of discriminating, the optimized model showed the highest identification accuracy of 96.7% for the top 5 ranking on a test data set of 30 hold-out samples. Neighborhood component analysis (NCA) on the experimental XRD samples shows that the samples from the same material are clustered in groups in the NCA map. Analysis on the class activation maps of the last CNN layer further discloses the mechanism by which the CNN model successfully identifies individual MOFs from the XRD patterns. Furthermore, this CNN model trained by the data augmentation technique would not only open numerous potential applications for identifying XRD patterns for different materials, but also pave avenues to autonomously analyze data by other characterization tools such as FTIR, Raman, and NMR spectroscopies.

36 MATERIALS SCIENCE↗

NASA NTRSDate not supplied

NASA GeneLab RNASeq Consensus Pipeline: A Nextflow Implementation

The NASA GeneLab project (genelab.nasa.gov) seeks to accelerate space biology research through cataloging and democratizing omics data. Since raw omics data is largely inaccessible to non-bioinformaticians, GeneLab works with the scientific community to develop standard processing pipelines to generate and publish processed data. Unlike raw data, processed data has greater immediate value to a wide range of users with varying technical backgrounds and computational capabilities. Standardizing processing workflows is essential to match the pace of raw data generation, ensure reproducibility, and enable standardized processed data for comparison across datasets. Previously, GeneLab developed a standardized pipeline for processing RNAseq data, referred to as the ‘GeneLab RNAseq Consensus Pipeline (RCP)’, in collaboration with GeneLab’s Analysis Working Groups. The work presented here is a Nextflow implementation of GeneLab’s RCP that automates and accelerates data processing of RNASeq datasets hosted on GeneLab. In addition to the core data processing, the workflow also includes staging of GeneLab raw data and a robust verification and validation (V&V) program that runs after each processing step to identify errors in real-time, stop additional downstream computation, and preserve computational resources. The workflow, including the staging and V&V functionality, is open source for others to reuse and modify at https://github.com/nasa/GeneLab_Data_Processing/tree/master/RNAseq.

Jonathan Dejesus Oribello↗

NASA NTRSDate not supplied

NASA GeneLab RNASeq Consensus Pipeline: A Nextflow Implementation

Jonathan D Oribello↗

NASA NTRS1976

Determination of the cosmological rate of change of G and the tidal accelerations of earth and moon from ancient and modern astronomical data

The theory and numerical analysis of ancient astronomical observations (1374 to 1715) are combined with modern data in a simultaneous solution for: the tidal acceleration of the lunar longitude; the observed apparent acceleration of the earth's rotation; the true nontidal geophysical part of this acceleration; and the rate of change in the gravitational constant. Provided are three independent determinations of a rate of change of G consistent with the Hubble Constant and a near zero nontidal rotational acceleration of the earth. The tidal accelerations are shown to have remained constant during the historical period within uncertainties. Ancient and modern solar system data, and extragalactic observations provided a completely consistent astronomical and cosmological scheme.

Muller, P. M.↗

DOE OSTI2021

Discovery of complex oxides via automated experiments and data science

Significance Automation is accelerating the discovery of useful materials, yet testing even a small fraction of the billions of possible materials for a desired property is beyond the reach of workflows involving resource-intensive property measurements. Due to relationships among composition, structure, and properties, identifying a complex material with one interesting property makes it the proverbial needle in a haystack that merits testing for additional properties. We accelerate materials synthesis and optical characterization by employing physics-aware data science to identify materials for further investigation. With this approach, one does not need high-throughput methods for measuring every material property of interest since a single ultra-high–throughput workflow can guide material selection for other properties, which is a new paradigm for accelerated materials discovery.

36 MATERIALS SCIENCE↗